SPAM E-MAIL CHARACTERIZATION: AN EXPERIMENTAL PERFORMANCE COMPARISON OF MACHINE LEARNING

The increasing volume of unsolicited mass e-mail (otherwise called spam) has generated a need for reliable against spam filters. Utilizing a classifier based on machine learning techniques to naturally filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning calculations applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful data for all the machine learning calculations considered to detect spam e-mail.